Ground nephogram identifying method solving problem of insensitiveness in illumination

Abstract

The invention discloses a ground nephogram identifying method solving the problem of insensitiveness in illumination. An Retinex algorithm is applied to reduce or eliminate the influence of sun light on a nephogram sample and obtain an enhanced nephogram image sample, so that the relevant characteristics of illumination invariants can be extracted easily, and the identifying rate of the nephogram can be increased; a clustering algorithm is applied to separate a cloud target from a background, and only the characteristic of the cloud target is extracted, and the characteristic value is computed for the identification of the cloud, so that the identifying accuracy is increased; an AdaBoost integration algorithm is applied to integrate a plurality of independent categorizers trained through the adoption of an SVM learning algorithm, parameters of the SVM algorithm are reasonably adjusted during the process of training data to enable the trained categorizers to have diversity, so that the nephogram identifying accuracy is increased, and the generalization performance is improved to a large extent.

Description

technical field [0001] The invention discloses a ground-based cloud image recognition method for solving the problem of insensitivity to illumination, and relates to the application of digital image processing technology in the field of meteorological observation. Background technique [0002] Clouds play an important role in atmospheric radiative transfer, and the shape, distribution, quantity and changes of clouds indicate the state of atmospheric motion. Different clouds have different radiation characteristics and distributions, so they are of great significance to service industries such as weather forecasting and flight support. At present, the general meteorological elements have basically achieved automatic observation, but the observation of ground-based cloud images cannot be fully automated, and still relies on manual observation. Due to the relatively small range of ground-based cloud observation, the contained texture information is relatively rich, and it has ...

AI Technical Summary

Problems solved by technology

Among them, the illumination regularization method is to use image processing technology to process the illumination image, such as histogram equalization, logarithmic transformation, etc., although this kind of method weakens the influence of illumination changes on the image to a certain extent, but in complex illumination conditions The effect under the condition is still unsatisfactory; the illumination invariant extraction method is to extract image features that do not change or change slightly with illumination changes, such as the Retinex theory of color constant perception, etc.

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